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Introduction

The ViViT (Video Vision Transformer) model is an extension of the Vision Transformer (ViT) architecture, specifically adapted for video classification tasks. It was introduced in the paper "ViViT: A Video Vision Transformer" by Arnab et al. and further developed within Google's research repository.

Architecture

ViViT adapts the Vision Transformer (ViT) approach to process video data. This involves handling the temporal dimension of videos alongside the spatial information typically managed by ViT. The architecture details are elaborated in the original paper, which can be accessed through arXiv.

Training

The model is primarily designed to be fine-tuned for specific downstream tasks such as video classification. Users can find various pre-trained and fine-tuned versions of the model on the Hugging Face model hub, which can be adapted to suit different video-related tasks.

Guide: Running Locally

1. Installation: Ensure you have Python and PyTorch installed. You will also need the transformers library from Hugging Face.
2. Setup: Clone the repository from Hugging Face or download the model files directly.
3. Load Model: Use the transformers library to load ViViT and prepare it for inference or further training.
4. Execution: Run the model on video input data to perform classification or other tasks.
5. Hardware: For efficient processing, especially for large videos, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The ViViT model is released under the MIT License, allowing for wide usage and modification with minimal restrictions.